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Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 pp 388–403Cite as

Continuous-Time Stereo Visual Odometry Based on Dynamics Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

Abstract

We propose a dynamics model to represent the camera trajectory as a continuous function of time and forces. Equipped with such a representation, we convert the classical visual odometry problem to analyzing the forces applied to the camera. In contrast to the classical discrete-time estimation strategy, the continuous nature of the camera motion is inherently revealed in the framework, and the camera motion can be simply modeled with only few parameters within time intervals. The dynamics model guarantees the continuous velocity, and hence assures a smooth trajectory, which is robust against noise and avoiding the pose vibration. Evaluations on real-world benchmark datasets show that our method outperforms other continuous-time methods.

This work is supported by the National Natural Science Foundation of China (61632003, 61771026), and National Key Research and Development Program of China (2017YFB1002601).

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, Beijing, China

    Xin Wang, Fei Xue, Zike Yan, Qiuyuan Wang & Hongbin Zha

  2. Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China

    Xin Wang, Fei Xue, Zike Yan, Qiuyuan Wang & Hongbin Zha

  3. Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Wei Dong

Authors
  1. Xin Wang
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  2. Fei Xue
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  3. Zike Yan
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  4. Wei Dong
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  5. Qiuyuan Wang
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  6. Hongbin Zha
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Corresponding authors

Correspondence to Xin Wang or Hongbin Zha .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Wang, X., Xue, F., Yan, Z., Dong, W., Wang, Q., Zha, H. (2019). Continuous-Time Stereo Visual Odometry Based on Dynamics Model. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_25

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  • DOI: https://doi.org/10.1007/978-3-030-20876-9_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20875-2

  • Online ISBN: 978-3-030-20876-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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